optimizer.zero_grad()

optimizer.zero_grad()意思是把梯度置零，也就是把loss关于weight的导数变成0.

在学习pytorch的时候注意到，对于每个batch大都执行了这样的操作：

        # zero the parameter gradients
        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

对于这些操作我是把它理解成一种梯度下降法，贴一个自己之前手写的简单梯度下降法作为对照：

    # gradient descent
    weights = [0] * n
    alpha = 0.0001
    max_Iter = 50000
    for i in range(max_Iter):
        loss = 0
        d_weights = [0] * n
        for k in range(m):
            h = dot(input[k], weights)
            d_weights = [d_weights[j] + (label[k] - h) * input[k][j] for j in range(n)] 
            loss += (label[k] - h) * (label[k] - h) / 2
        d_weights = [d_weights[k]/m for k in range(n)]
        weights = [weights[k] + alpha * d_weights[k] for k in range(n)]
        if i%10000 == 0:
            print "Iteration %d loss: %f"%(i, loss/m)
            print weights

可以发现它们实际上是一一对应的：

optimizer.zero_grad()对应d_weights = [0] * n

即将梯度初始化为零（因为一个batch的loss关于weight的导数是所有sample的loss关于weight的导数的累加和）

outputs = net(inputs)对应h = dot(input[k], weights)

即前向传播求出预测的值

loss = criterion(outputs, labels)对应loss += (label[k] - h) * (label[k] - h) / 2

这一步很明显，就是求loss（其实我觉得这一步不用也可以，反向传播时用不到loss值，只是为了让我们知道当前的loss是多少）
loss.backward()对应d_weights = [d_weights[j] + (label[k] - h) * input[k][j] for j in range(n)]

即反向传播求梯度
optimizer.step()对应weights = [weights[k] + alpha * d_weights[k] for k in range(n)]

即更新所有参数